Purpose

Estimation of the amount of waste to be generated in the coming years is critical for the evaluation of existing waste treatment service capacities. This study was conducted to evaluate the performance of various mathematical modeling methods to forecast medical waste generation of Istanbul, the largest city in Turkey.

Methods

Autoregressive Integrated Moving Average (ARIMA), Support Vector Regression (SVR), Grey Modeling (1,1) and Linear Regression (LR) analysis were used to estimate annual medical waste generation from 2018 to 2023. A 23-year data from 1995 to 2017 provided from the Istanbul Metropolitan Municipality’s affiliated environmental company ISTAC Company were utilized to examine the forecasting accuracy of methods. Different performance measures such as mean absolute deviation (MAD), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R²) were used to evaluate the performance of these models.

Results

ARIMA (0,1,2) model with the lowest RMSE (763.6852), MAD (588.4712), and MAPE (11.7595) values and the highest R² (0.9888) value showed a superior prediction performance compared to SVR, Grey Modeling (1,1), and LR analysis. The results obtained from the models indicated that the total amount of annual medical waste to be generated will increase from about 26,400 tons in 2017 to 35,600 tons in 2023.

Conclusions

ARIMA (0,1,2) model developed in this study can help decision-makers to take better measures and develop policies regarding waste management practices in the future.

中文翻译：

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使用 SVR、GM (1,1) 和 ARIMA 模型预测医疗废物产生：大城市伊斯坦布尔的案例研究

目的

估计未来几年将产生的废物量对于评估现有废物处理服务能力至关重要。本研究旨在评估各种数学建模方法的性能，以预测土耳其最大城市伊斯坦布尔的医疗废物产生。

方法

自回归综合移动平均 (ARIMA)、支持向量回归 (SVR)、灰色模型 (1,1) 和线性回归 (LR) 分析用于估计 2018 年至 2023 年的医疗废物年产生量。从 1995 年到 23 年的数据2017 年由伊斯坦布尔大都会市附属环境公司 ISTAC 公司提供，用于检查方法的预测准确性。使用不同的性能度量，例如平均绝对偏差 (MAD)、平均绝对百分比误差 (MAPE)、均方根误差 (RMSE) 和决定系数 (R ² ) 来评估这些模型的性能。

结果

ARIMA (0,1,2) 模型与^SVR、灰色建模 (1 ,1) 和 LR 分析。从模型中获得的结果表明，每年产生的医疗废物总量将从 2017 年的约 2.64 万吨增加到 2023 年的 3.56 万吨。

结论

本研究开发的 ARIMA (0,1,2) 模型可以帮助决策者在未来采取更好的措施并制定有关废物管理实践的政策。